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A Causal Probabilitic Network for Optimal Treatment of Bachterial Infections

A Causal Probabilitic Network for Optimal Treatment of Bachterial Infections. Alicia Ruvinsky Scott Langevin. Problem: Bacterial Infections. 30 percent mortality rate from severe bacterial infection 1/3 given inappropriate treatment 20% prescribed superfluous drugs

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A Causal Probabilitic Network for Optimal Treatment of Bachterial Infections

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  1. A Causal Probabilitic Network for Optimal Treatment of Bachterial Infections Alicia Ruvinsky Scott Langevin

  2. Problem: Bacterial Infections • 30 percent mortality rate from severe bacterial infection • 1/3 given inappropriate treatment • 20% prescribed superfluous drugs • Anti-biotic drugs account for 20-50 percent of hospitals drug expenses • Bacterial resistance to anti-biotic treatment aggravated by miss-diagnoses

  3. Research Objectives Build a Decision Support System to provide: • Likelihood of a bacterial infection • Measure of its severity • Most likely site of infection • Most likely pathogen • Susceptibility of pathogen to drugs • Gain in life expectancy through treatment • Cost of drug treatment (price, side-effect, ecological impact, future resistance) • Ranking of anti-biotic drugs (Cost-Benefit)

  4. Problems with initial BN Approach • Model is not portable (specific to region/hospital) • Dichotomous data (local vs universal) • Human input error (20% of cases) Obviating Enhancements • Fix by normalizing system (localizing model) • Fix by objective input requirements (symptoms, test results, etc)

  5. General Modularized Design Nodes in BN: • Pathogen - Represent the potential pathogens of infections at the given site • M_Distrib – Major patient-groups exhibiting a particular pathogen within the given site • Minor – minor distribution factors; factors that change the likelihood of one or more pathogens without affecting the overall risk for infection. • Infection – the different patterns in which infections manifest • Local-respo – local responses caused by an infection • Local-sign – manifestation of local responses. • Sys-respo – systemic response caused by infection and common to all sites of infections • Sign – manifestations of sys-respo • Spec-cultu – ability of pathogen to grow in local specimen • Blood-cultu – ability of pathogen to grow in the blood • Lab-site – ability of pathogen to grow at local site • Antibiotic_tr – antibiotic treatment prescribed for an infection • Coverage – the percentage of pathogens of a given infection susceptible to an antibiotic drug • Resistance – in-vitro susceptibility of pathogen to treatment • Cost – accounts for cost of using antibiotic: cost of purchase, side effects, and ecological impact • Gain – net gain in life expectancy gotten by prescribing an antibiotic drug • Underlying – disorders of the patient

  6. General Scheme for Site of Infection Network

  7. Urinary Tract Infections Network

  8. Calibrating the System

  9. Results • Addresses all important decision-points in first days of patient care • Expect the system to perform better than clinician • No test data showing it improves clinical practice and patient outcome - need a clinical trial • Convenience of calibrating system for new locations?

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